Graph neural networks (GNNs) have attracted growing interest in the past few years due to their universal applicability in various fields, e.g., social networks [1]} and natural language processing [2]}. Graph neural networks (GNNs) learn a lower-dimensional representation for a node in a vector space by aggregating the information from its neighbors using discrete hidden layers. Then the embedding can be used for downstream tasks such as node classification [3]}, link prediction [4]}, [5]}, and knowledge completion [6]}.
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